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An adaptive path planning algorithm with singularity consistency for robotic arms
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AI
DOI：10.1017/S0263574725102890.png)
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Abstract
En 中文
The study presents a novel approach to address challenges posed by singularities in robotic arm motion, focusing on Cartesian path planning and geometric path adherence. Recognizing limitations in traditional singularity avoidance methods, the research proposes a comprehensive strategy: reconstructing motion patterns in singular regions through singularity-consistent representations, applying arc-length reparameterization to Cartesian geometric paths, and incorporating path curvature as a dynamic weighting factor for sampling interval adjustment. This method achieves a balance between joint velocity smoothness and geometric tracking accuracy in Cartesian space, significantly enhancing the robot's ability to adhere to prescribed geometric paths, particularly near singularities. Experimental results demonstrate the efficacy of the proposed approach in facilitating smooth singularity transitions, improving joint velocity continuity, and enhancing geometric path adherence. The study contributes to robotic arm path planning by offering a practical solution for applications requiring precise trajectory following and effective singularity handling.
Keywords:
singularity consistency
path arc-length parameterization
weights adaptive sampling